Pengembangan Model Prediktif untuk Promosi Karyawan Menggunakan Algoritma Support Vector Machine, K-Nearest Neighbor, dan Random Forest
Development of Predictive Models for Employee Promotion Using Support Vector Machine, K-Nearest Neighbor, and Random Forest Algorithms
DOI:
https://doi.org/10.57152/malcom.v6i1.1914Keywords:
CRISP-DM, Machine Learning, Prediksi, Promosi KaryawanAbstract
Penilaian Kinerja Karyawan (EPA) mengalami perkembangan secara signifikan dari awalnya hanya berfokus pada penilaian prestasi menjadi penilaian komprehensif tentang seberapa baik kinerja karyawan. Kompleksitas penilaian ini dilakukan dengan mengevaluasi berdasarkan kualifikasi profesional, karakteristik pribadi, dan hasil pekerjaan karyawan. Penelitian ini mengembangkan model prediktif untuk promosi karyawan dengan menggabungkan algoritma Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), dan Random Forest. Studi ini menggunakan metode Cross Industry Standard Process for Data Mining (CRISP-DM) yang berfokus pada atribut seperti pendidikan, saluran rekrutmen, riwayat pelatihan, metrik kinerja, dan masa kerja. Hasil menunjukkan bahwa model Random Forest memberikan akurasi tertinggi sebesar 94,59%. Fitur KPI >80% dan penghargaan yang diterima memiliki bobot signifikan dalam menentukan keputusan promosi. Temuan ini dapat dijadikan sebagai landasan untuk menyusun kebijakan promosi karyawan yang lebih objektif dan efektif.
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